Courting New Solutions Using Problem-Solving Justice: Key Components, Guiding Principles, Strategies, Responses, Models, Approaches, Blueprints and Tool Kits
Bibliographic record
Abstract
Problem-solving courts began with the creation of the first adult drug treatment court in Miami, Florida, in 1989. This court spawned a movement of drug courts in the United States, which now number in excess of 2,300, plus more than 1,200 other types of courts using similar principles. These “problem-solving courts,” as they have come to be known, now number more than 3,500 in the United States including at least one drug court in every state, Federal district court programs, over seventy Tribal Healing-to-Wellness courts, and international courts in some twenty countries; the first international court having been established in Toronto, Ontario, Canada, in 1998. Beginning with the key components of drug courts that were published in 1994, attempts to define what exactly constitutes a problem-solving court began to proliferate at the turn of the Century. Con-currently, there is a movement to bring problem-solving courts to scale, in essence turning every court into a problem-solving court. Some authors have concentrated on these efforts rather than seeking to define specific types of court. Based on a literature review, this article explores the international principles as noted by various authors and stresses both the similarities and differences in those standards. It defines the types of problem-solving courts and reviews the general principles of them. As these tenets are applied to the traditional court setting, there is a shift to notions such as therapeutic jurisprudence and procedural fairness. The article concludes that we can define such principles while allowing differences to emerge when the legal culture so dictates and that these principles are transferable to non-specialty courts. Moreover, problem-solving courts are requiring a whole new set of skills and behaviors from judges including emotional intelligence and motivational interviewing, as well as knowledge of the stages of behavioral change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".